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Robust Group-Level Inference in Neuroimaging Genetic Studies

Abstract : Gene-neuroimaging studies involve high-dimensional data that have a complex statistical structure and that are likely to be contaminated with outliers. Robust, outlier-resistant methods are an alternative to prior outliers removal, which is a difficult task under high-dimensional unsupervised settings. In this work, we consider robust regression and its application to neuroimaging through an example gene-neuroimaging study on a large cohort of 300 subjects. We use randomized brain parcellation to sample a set of adapted low-dimensional spatial models to analyse the data. We combine this approach with robust regression in an analysis method that we show is outperforming state-of-the-art neuroimaging analysis methods.
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Contributor : Virgile Fritsch Connect in order to contact the contributor
Submitted on : Thursday, June 13, 2013 - 5:17:24 PM
Last modification on : Saturday, June 25, 2022 - 8:27:20 PM
Long-term archiving on: : Tuesday, April 4, 2017 - 9:47:43 PM


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  • HAL Id : hal-00833953, version 1



Virgile Fritsch, Benoit da Mota, Gaël Varoquaux, Vincent Frouin, Eva Loth, et al.. Robust Group-Level Inference in Neuroimaging Genetic Studies. Pattern Recognition in Neuroimaging, Jun 2013, Philadelphie, United States. ⟨hal-00833953⟩



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